Evaluating The Embedding Space of Foundation Models for Dermatological Images to Guide Backbone Selection for A Fine-Tuning Pipeline
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901268794966016 |
|---|---|
| author | Thi Anh Thu Pham Khoi Nguyen Gia Tran Hieu Tran Anh Duc Thi Thanh Tu Bui |
| author_facet | Thi Anh Thu Pham Khoi Nguyen Gia Tran Hieu Tran Anh Duc Thi Thanh Tu Bui |
| contents | In recent years, computer vision models have advanced rapidly and have been increasingly applied in dermatology. However, in practice, many skin diseases present with very similar visual manifestations, making image representation in vector form not always accurately reflect the true nature of the disease. Therefore, this study focuses on comparing two approaches: general-purpose models designed for multiple tasks and models specifically trained for medical and dermatological domains.Experiments were conducted on 1,300 dermatological images covering 13 different infectious diseases, with a balanced number of images across disease categories. The models were evaluated based on their ability to retrieve images with similar characteristics, while also considering computational cost.The results show that in terms of pure representation quality, an ultra-large-scale general-purpose model achieved the highest performance. However, dermatology-specific models provided an optimal balance between performance and computational cost for the infectious disease group, producing stable and clinically appropriate image representations to serve as a foundation for subsequent clinical research. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19388449 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Evaluating The Embedding Space of Foundation Models for Dermatological Images to Guide Backbone Selection for A Fine-Tuning Pipeline Thi Anh Thu Pham Khoi Nguyen Gia Tran Hieu Tran Anh Duc Thi Thanh Tu Bui Dermatology Embedding; Embedding Evaluation; Foundation Models; Backbone Selection; Skin Retrieval In recent years, computer vision models have advanced rapidly and have been increasingly applied in dermatology. However, in practice, many skin diseases present with very similar visual manifestations, making image representation in vector form not always accurately reflect the true nature of the disease. Therefore, this study focuses on comparing two approaches: general-purpose models designed for multiple tasks and models specifically trained for medical and dermatological domains.Experiments were conducted on 1,300 dermatological images covering 13 different infectious diseases, with a balanced number of images across disease categories. The models were evaluated based on their ability to retrieve images with similar characteristics, while also considering computational cost.The results show that in terms of pure representation quality, an ultra-large-scale general-purpose model achieved the highest performance. However, dermatology-specific models provided an optimal balance between performance and computational cost for the infectious disease group, producing stable and clinically appropriate image representations to serve as a foundation for subsequent clinical research. |
| title | Evaluating The Embedding Space of Foundation Models for Dermatological Images to Guide Backbone Selection for A Fine-Tuning Pipeline |
| topic | Dermatology Embedding; Embedding Evaluation; Foundation Models; Backbone Selection; Skin Retrieval |
| url | https://doi.org/10.5281/zenodo.19388449 |